Overview

Brought to you by YData

Dataset statistics

Number of variables59
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.5 MiB
Average record size in memory472.0 B

Variable types

Numeric15
Categorical44

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Avg_Hillshade is highly overall correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Cover_Type is highly overall correlated with Soil_Type_9 and 1 other fieldsHigh correlation
Distance_to_Water is highly overall correlated with Horizontal_Distance_To_Hydrology and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type_39 and 2 other fieldsHigh correlation
Elevation_x_Slope is highly overall correlated with SlopeHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 3 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Hydro_Road_Fire_Distance is highly overall correlated with Horizontal_Distance_To_Fire_Points and 3 other fieldsHigh correlation
Slope is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Soil_Type_28 is highly overall correlated with Wilderness_Area_0High correlation
Soil_Type_39 is highly overall correlated with ElevationHigh correlation
Soil_Type_9 is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Wilderness_Area_0 is highly overall correlated with Hydro_Road_Fire_Distance and 2 other fieldsHigh correlation
Wilderness_Area_2 is highly overall correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly overall correlated with Cover_Type and 3 other fieldsHigh correlation
Wilderness_Area_1 is highly imbalanced (87.8%) Imbalance
Wilderness_Area_3 is highly imbalanced (62.2%) Imbalance
Soil_Type_0 is highly imbalanced (94.7%) Imbalance
Soil_Type_1 is highly imbalanced (92.1%) Imbalance
Soil_Type_2 is highly imbalanced (95.5%) Imbalance
Soil_Type_3 is highly imbalanced (88.7%) Imbalance
Soil_Type_4 is highly imbalanced (96.9%) Imbalance
Soil_Type_5 is highly imbalanced (90.0%) Imbalance
Soil_Type_6 is highly imbalanced (99.7%) Imbalance
Soil_Type_7 is highly imbalanced (99.5%) Imbalance
Soil_Type_8 is highly imbalanced (97.7%) Imbalance
Soil_Type_9 is highly imbalanced (70.0%) Imbalance
Soil_Type_10 is highly imbalanced (88.0%) Imbalance
Soil_Type_11 is highly imbalanced (67.5%) Imbalance
Soil_Type_12 is highly imbalanced (82.4%) Imbalance
Soil_Type_13 is highly imbalanced (99.1%) Imbalance
Soil_Type_14 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_15 is highly imbalanced (95.4%) Imbalance
Soil_Type_16 is highly imbalanced (94.5%) Imbalance
Soil_Type_17 is highly imbalanced (96.5%) Imbalance
Soil_Type_18 is highly imbalanced (94.2%) Imbalance
Soil_Type_19 is highly imbalanced (87.5%) Imbalance
Soil_Type_20 is highly imbalanced (98.2%) Imbalance
Soil_Type_21 is highly imbalanced (71.0%) Imbalance
Soil_Type_22 is highly imbalanced (54.3%) Imbalance
Soil_Type_23 is highly imbalanced (78.8%) Imbalance
Soil_Type_24 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_25 is highly imbalanced (95.8%) Imbalance
Soil_Type_26 is highly imbalanced (98.8%) Imbalance
Soil_Type_27 is highly imbalanced (98.0%) Imbalance
Soil_Type_29 is highly imbalanced (67.4%) Imbalance
Soil_Type_30 is highly imbalanced (72.2%) Imbalance
Soil_Type_31 is highly imbalanced (55.7%) Imbalance
Soil_Type_32 is highly imbalanced (64.5%) Imbalance
Soil_Type_33 is highly imbalanced (98.5%) Imbalance
Soil_Type_34 is highly imbalanced (97.3%) Imbalance
Soil_Type_35 is highly imbalanced (99.7%) Imbalance
Soil_Type_36 is highly imbalanced (99.3%) Imbalance
Soil_Type_37 is highly imbalanced (82.2%) Imbalance
Soil_Type_38 is highly imbalanced (85.5%) Imbalance
Soil_Type_39 is highly imbalanced (89.7%) Imbalance
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros
Distance_to_Water has 21630 (4.3%) zeros Zeros

Reproduction

Analysis started2025-06-09 19:31:36.572035
Analysis finished2025-06-09 19:33:11.920507
Duration1 minute and 35.35 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87896182
Minimum0
Maximum1
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.003159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.73622047
Q10.83858268
median0.88976378
Q30.93307087
95-th percentile0.98425197
Maximum1
Range1
Interquartile range (IQR)0.094488189

Descriptive statistics

Standard deviation0.077089647
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean0.87896182
Median Absolute Deviation (MAD)0.047244094
Skewness-1.1404263
Sum444186.87
Variance0.0059428137
MonotonicityNot monotonic
2025-06-09T22:33:12.092005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9094488189 12314
 
2.4%
0.8976377953 12230
 
2.4%
0.9173228346 12063
 
2.4%
0.905511811 11975
 
2.4%
0.9015748031 11849
 
2.3%
0.9212598425 11805
 
2.3%
0.8937007874 11601
 
2.3%
0.8897637795 11594
 
2.3%
0.8779527559 11564
 
2.3%
0.8858267717 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
0.1181102362 1
 
< 0.1%
0.157480315 1
 
< 0.1%
0.1653543307 1
 
< 0.1%
0.1771653543 1
 
< 0.1%
0.2086614173 2
 
< 0.1%
0.2480314961 1
 
< 0.1%
0.2519685039 1
 
< 0.1%
0.2677165354 1
 
< 0.1%
0.2795275591 1
 
< 0.1%
ValueCountFrequency (%)
1 3981
0.8%
0.9960629921 4664
0.9%
0.9921259843 5492
1.1%
0.9881889764 5864
1.2%
0.9842519685 6488
1.3%
0.9803149606 6356
1.3%
0.9763779528 6907
1.4%
0.9724409449 7617
1.5%
0.968503937 7453
1.5%
0.9645669291 7352
1.5%

Hydro_Road_Fire_Distance
Real number (ℝ)

High correlation 

Distinct12679
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35297117
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.177306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.10281593
Q10.2065526
median0.31934321
Q30.46919359
95-th percentile0.72792143
Maximum1
Range1
Interquartile range (IQR)0.26264099

Descriptive statistics

Standard deviation0.19013593
Coefficient of variation (CV)0.53867268
Kurtosis-0.029145393
Mean0.35297117
Median Absolute Deviation (MAD)0.12460677
Skewness0.7353494
Sum178375.39
Variance0.03615167
MonotonicityNot monotonic
2025-06-09T22:33:12.260441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.218061843 134
 
< 0.1%
0.2219749866 125
 
< 0.1%
0.2185222128 123
 
< 0.1%
0.215299624 122
 
< 0.1%
0.4014424921 121
 
< 0.1%
0.2309521983 118
 
< 0.1%
0.2234328244 117
 
< 0.1%
0.2638686411 116
 
< 0.1%
0.2367835495 116
 
< 0.1%
0.1566024707 116
 
< 0.1%
Other values (12669) 504146
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.0005370981355 1
< 0.1%
0.001304381186 1
< 0.1%
0.003222588813 2
< 0.1%
0.003376045423 1
< 0.1%
0.003759686949 1
< 0.1%
0.004143328474 1
< 0.1%
0.004296785084 1
< 0.1%
0.004450241694 2
< 0.1%
0.005524437965 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9994629019 1
< 0.1%
0.9989258037 1
< 0.1%
0.9986956188 1
< 0.1%
0.9984654339 1
< 0.1%
0.9979283358 1
< 0.1%
0.9978516075 1
< 0.1%
0.9976981508 1
< 0.1%
0.9976214225 2
< 0.1%
0.9975446942 2
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

High correlation 

Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34171421
Minimum0
Maximum1
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.343954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.054938879
Q10.16017985
median0.29197696
Q30.48826753
95-th percentile0.78277364
Maximum1
Range1
Interquartile range (IQR)0.32808768

Descriptive statistics

Standard deviation0.22487219
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean0.34171421
Median Absolute Deviation (MAD)0.15245188
Skewness0.65607631
Sum172686.64
Variance0.050567502
MonotonicityNot monotonic
2025-06-09T22:33:12.429215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02107629619 1078
 
0.2%
0.08683434031 882
 
0.2%
0.1264577772 821
 
0.2%
0.1433188141 805
 
0.2%
0.1391035549 777
 
0.2%
0.1348882956 764
 
0.2%
0.0547983701 763
 
0.2%
0.1601798511 736
 
0.1%
0.1475340733 726
 
0.1%
0.105381481 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
0.004215259238 267
0.1%
0.005901362934 153
 
< 0.1%
0.008430518477 280
0.1%
0.009414078966 249
< 0.1%
0.01194323451 293
0.1%
0.01264577772 331
0.1%
0.01334832092 317
0.1%
0.01517493326 537
0.1%
0.01686103695 559
0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9998594914 1
< 0.1%
0.9992974568 1
< 0.1%
0.9971898272 1
< 0.1%
0.996487284 1
< 0.1%
0.9957847408 2
< 0.1%
0.9950821976 1
< 0.1%
0.9946606716 1
< 0.1%
0.994520163 2
< 0.1%
0.9932555852 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83575869
Minimum0
Maximum1
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.510218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.62992126
Q10.78346457
median0.85826772
Q30.90944882
95-th percentile0.96456693
Maximum1
Range1
Interquartile range (IQR)0.12598425

Descriptive statistics

Standard deviation0.10484011
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean0.83575869
Median Absolute Deviation (MAD)0.059055118
Skewness-1.2439666
Sum422354
Variance0.010991448
MonotonicityNot monotonic
2025-06-09T22:33:12.590355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8897637795 10413
 
2.1%
0.8976377953 10242
 
2.0%
0.8818897638 10051
 
2.0%
0.905511811 10049
 
2.0%
0.8779527559 9782
 
1.9%
0.874015748 9720
 
1.9%
0.9173228346 9416
 
1.9%
0.8937007874 9382
 
1.9%
0.8858267717 9243
 
1.8%
0.8700787402 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
0.1417322835 1
 
< 0.1%
0.1811023622 2
 
< 0.1%
0.1968503937 1
 
< 0.1%
0.2047244094 1
 
< 0.1%
0.2086614173 1
 
< 0.1%
0.2125984252 3
 
< 0.1%
0.2165354331 1
 
< 0.1%
0.2204724409 5
 
< 0.1%
0.2244094488 2
 
< 0.1%
ValueCountFrequency (%)
1 1641
 
0.3%
0.9960629921 1822
 
0.4%
0.9921259843 2123
0.4%
0.9881889764 2409
0.5%
0.9842519685 2766
0.5%
0.9803149606 3116
0.6%
0.9763779528 3249
0.6%
0.9724409449 3691
0.7%
0.968503937 4069
0.8%
0.9645669291 4560
0.9%

Avg_Hillshade
Real number (ℝ)

High correlation 

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88483176
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.672629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.73443223
Q10.84615385
median0.8992674
Q30.93956044
95-th percentile0.98534799
Maximum1
Range1
Interquartile range (IQR)0.093406593

Descriptive statistics

Standard deviation0.07908722
Coefficient of variation (CV)0.089381081
Kurtosis3.0701699
Mean0.88483176
Median Absolute Deviation (MAD)0.045787546
Skewness-1.3616708
Sum447153.27
Variance0.0062547883
MonotonicityNot monotonic
2025-06-09T22:33:12.756138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9212454212 6583
 
1.3%
0.9139194139 6186
 
1.2%
0.9175824176 6168
 
1.2%
0.9322344322 5970
 
1.2%
0.9047619048 5963
 
1.2%
0.9249084249 5952
 
1.2%
0.9010989011 5878
 
1.2%
0.8992673993 5798
 
1.1%
0.9065934066 5781
 
1.1%
0.9285714286 5696
 
1.1%
Other values (374) 445379
88.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.01465201465 1
< 0.1%
0.13003663 1
< 0.1%
0.1538461538 1
< 0.1%
0.1648351648 2
< 0.1%
0.1758241758 1
< 0.1%
0.1794871795 1
< 0.1%
0.2106227106 1
< 0.1%
0.2289377289 1
< 0.1%
0.2435897436 1
< 0.1%
ValueCountFrequency (%)
1 247
 
< 0.1%
0.9981684982 1927
0.4%
0.9963369963 2939
0.6%
0.9945054945 3181
0.6%
0.9926739927 3415
0.7%
0.9908424908 3115
0.6%
0.989010989 3447
0.7%
0.9871794872 3739
0.7%
0.9853479853 3372
0.7%
0.9835164835 4121
0.8%

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54656732
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:12.840658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26113057
Q10.47773887
median0.56428214
Q30.64182091
95-th percentile0.73186593
Maximum1
Range1
Interquartile range (IQR)0.16408204

Descriptive statistics

Standard deviation0.13781103
Coefficient of variation (CV)0.25213916
Kurtosis1.0931128
Mean0.54656732
Median Absolute Deviation (MAD)0.08154077
Skewness-0.89179409
Sum276209.98
Variance0.018991879
MonotonicityNot monotonic
2025-06-09T22:33:12.922699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5547773887 1625
 
0.3%
0.5517758879 1613
 
0.3%
0.5662831416 1613
 
0.3%
0.5567783892 1609
 
0.3%
0.5597798899 1603
 
0.3%
0.5582791396 1599
 
0.3%
0.5647823912 1554
 
0.3%
0.5482741371 1524
 
0.3%
0.5532766383 1519
 
0.3%
0.5467733867 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0005002501251 1
 
< 0.1%
0.00100050025 1
 
< 0.1%
0.0020010005 1
 
< 0.1%
0.003501750875 1
 
< 0.1%
0.004002001001 1
 
< 0.1%
0.004502251126 1
 
< 0.1%
0.006003001501 3
< 0.1%
0.006503251626 4
< 0.1%
0.007003501751 1
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.9994997499 1
 
< 0.1%
0.9989994997 1
 
< 0.1%
0.9974987494 1
 
< 0.1%
0.9969984992 1
 
< 0.1%
0.9964982491 2
 
< 0.1%
0.995997999 1
 
< 0.1%
0.9954977489 4
< 0.1%
0.9949974987 1
 
< 0.1%
0.9939969985 6
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42996532
Minimum0
Maximum1
Zeros4536
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.029211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.030555556
Q10.15833333
median0.34444444
Q30.73055556
95-th percentile0.95555556
Maximum1
Range1
Interquartile range (IQR)0.57222222

Descriptive statistics

Standard deviation0.3129525
Coefficient of variation (CV)0.72785521
Kurtosis-1.2289254
Mean0.42996532
Median Absolute Deviation (MAD)0.23333333
Skewness0.41649173
Sum217284.69
Variance0.097939265
MonotonicityNot monotonic
2025-06-09T22:33:13.124734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.125 5854
 
1.2%
0 4536
 
0.9%
0.25 4111
 
0.8%
0.375 3443
 
0.7%
0.175 3320
 
0.7%
0.875 3236
 
0.6%
0.05 3077
 
0.6%
0.2 3071
 
0.6%
0.075 3071
 
0.6%
0.09444444444 2521
 
0.5%
Other values (351) 469114
92.8%
ValueCountFrequency (%)
0 4536
0.9%
0.002777777778 1506
 
0.3%
0.005555555556 1709
 
0.3%
0.008333333333 1718
 
0.3%
0.01111111111 2023
0.4%
0.01388888889 1844
0.4%
0.01666666667 1981
0.4%
0.01944444444 1951
0.4%
0.02222222222 1983
0.4%
0.025 2229
0.4%
ValueCountFrequency (%)
1 49
 
< 0.1%
0.9972222222 1220
0.2%
0.9944444444 1566
0.3%
0.9916666667 1643
0.3%
0.9888888889 1801
0.4%
0.9861111111 1725
0.3%
0.9833333333 1784
0.4%
0.9805555556 1732
0.3%
0.9777777778 1782
0.4%
0.975 1938
0.4%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26974003
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.217684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.21842105
median0.24736842
Q30.29736842
95-th percentile0.425
Maximum1
Range1
Interquartile range (IQR)0.078947368

Descriptive statistics

Standard deviation0.076185672
Coefficient of variation (CV)0.2824411
Kurtosis5.8017203
Mean0.26974003
Median Absolute Deviation (MAD)0.034210526
Skewness1.8979445
Sum136314.2
Variance0.0058042566
MonotonicityNot monotonic
2025-06-09T22:33:13.306686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2092105263 34287
 
6.8%
0.2131578947 8388
 
1.7%
0.2223684211 8084
 
1.6%
0.2184210526 7807
 
1.5%
0.2263157895 7707
 
1.5%
0.2171052632 7694
 
1.5%
0.2144736842 7529
 
1.5%
0.2157894737 6717
 
1.3%
0.2302631579 6715
 
1.3%
0.2394736842 6539
 
1.3%
Other values (682) 403887
79.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.001315789474 1
< 0.1%
0.003947368421 1
< 0.1%
0.006578947368 1
< 0.1%
0.007894736842 2
< 0.1%
0.009210526316 2
< 0.1%
0.01052631579 1
< 0.1%
0.01184210526 1
< 0.1%
0.01315789474 1
< 0.1%
0.01578947368 1
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9973684211 1
 
< 0.1%
0.9960526316 2
< 0.1%
0.9947368421 3
< 0.1%
0.9921052632 2
< 0.1%
0.9881578947 1
 
< 0.1%
0.9868421053 1
 
< 0.1%
0.9855263158 2
< 0.1%
0.9842105263 3
< 0.1%
0.9828947368 3
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19052047
Minimum0
Maximum1
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.394202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.021474588
Q10.077308518
median0.15461704
Q30.27129563
95-th percentile0.48246242
Maximum1
Range1
Interquartile range (IQR)0.19398712

Descriptive statistics

Standard deviation0.15110145
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean0.19052047
Median Absolute Deviation (MAD)0.093772369
Skewness1.188776
Sum96280.281
Variance0.022831649
MonotonicityNot monotonic
2025-06-09T22:33:13.476490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0214745884 29993
 
5.9%
0 21630
 
4.3%
0.107372942 18274
 
3.6%
0.04294917681 16778
 
3.3%
0.0479599141 13454
 
2.7%
0.03006442377 12960
 
2.6%
0.07730851825 12702
 
2.5%
0.06084466714 12156
 
2.4%
0.06442376521 9757
 
1.9%
0.08589835361 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
0.0214745884 29993
5.9%
0.03006442377 12960
2.6%
0.04294917681 16778
3.3%
0.0479599141 13454
2.7%
0.06084466714 12156
2.4%
0.06442376521 9757
 
1.9%
0.06800286328 8120
 
1.6%
0.07730851825 12702
2.5%
0.08589835361 9343
 
1.8%
ValueCountFrequency (%)
1 1
< 0.1%
0.9949892627 2
< 0.1%
0.9899785254 2
< 0.1%
0.9892627058 1
< 0.1%
0.9849677881 1
< 0.1%
0.9813886901 1
< 0.1%
0.9806728704 1
< 0.1%
0.9799570508 1
< 0.1%
0.9792412312 2
< 0.1%
0.9742304939 2
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

High correlation 

Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28023646
Minimum0
Maximum1
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.556744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.056879967
Q10.14275756
median0.24048515
Q30.36135508
95-th percentile0.70946605
Maximum1
Range1
Interquartile range (IQR)0.21859752

Descriptive statistics

Standard deviation0.18929568
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean0.28023646
Median Absolute Deviation (MAD)0.10748641
Skewness1.266097
Sum141618.62
Variance0.035832855
MonotonicityNot monotonic
2025-06-09T22:33:13.639257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08615641991 1244
 
0.2%
0.07542172034 981
 
0.2%
0.0846228914 930
 
0.2%
0.1313258051 856
 
0.2%
0.1389934477 844
 
0.2%
0.09758817789 833
 
0.2%
0.1012128816 798
 
0.2%
0.1048375854 782
 
0.2%
0.1254705144 774
 
0.2%
0.1338352154 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
0.004182350481 184
< 0.1%
0.005855290673 183
< 0.1%
0.008364700962 182
< 0.1%
0.009340582741 370
0.1%
0.01184999303 183
< 0.1%
0.01254705144 182
< 0.1%
0.01324410986 366
0.1%
0.01505646173 369
0.1%
0.01672940192 180
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9998605883 1
< 0.1%
0.9993029416 1
< 0.1%
0.9967935313 1
< 0.1%
0.9960964729 1
< 0.1%
0.9956782378 1
< 0.1%
0.9955388262 2
< 0.1%
0.9953994145 1
< 0.1%
0.9941447093 1
< 0.1%
0.9934476509 1
< 0.1%

Slope
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20859689
Minimum0
Maximum1
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.719517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.060606061
Q10.12121212
median0.1969697
Q30.27272727
95-th percentile0.42424242
Maximum1
Range1
Interquartile range (IQR)0.15151515

Descriptive statistics

Standard deviation0.11323937
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean0.20859689
Median Absolute Deviation (MAD)0.075757576
Skewness0.85685891
Sum105415.27
Variance0.012823154
MonotonicityNot monotonic
2025-06-09T22:33:13.803523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1515151515 30511
 
6.0%
0.1666666667 30123
 
6.0%
0.1818181818 29324
 
5.8%
0.1363636364 29017
 
5.7%
0.196969697 28187
 
5.6%
0.1212121212 27610
 
5.5%
0.2121212121 25932
 
5.1%
0.2272727273 24691
 
4.9%
0.1060606061 24304
 
4.8%
0.09090909091 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
0.01515151515 3485
 
0.7%
0.0303030303 7301
 
1.4%
0.04545454545 10951
 
2.2%
0.06060606061 15310
3.0%
0.07575757576 19417
3.8%
0.09090909091 22768
4.5%
0.1060606061 24304
4.8%
0.1212121212 27610
5.5%
0.1363636364 29017
5.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9848484848 2
 
< 0.1%
0.9696969697 1
 
< 0.1%
0.9545454545 1
 
< 0.1%
0.9393939394 2
 
< 0.1%
0.9242424242 4
< 0.1%
0.9090909091 2
 
< 0.1%
0.8939393939 3
< 0.1%
0.8787878788 1
 
< 0.1%
0.8636363636 7
< 0.1%

Elevation_x_Slope
Real number (ℝ)

High correlation 

Distinct37825
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19572668
Minimum0
Maximum1
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:13.885716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.054874073
Q10.12053885
median0.18209684
Q30.25631101
95-th percentile0.3833073
Maximum1
Range1
Interquartile range (IQR)0.13577216

Descriptive statistics

Standard deviation0.10242268
Coefficient of variation (CV)0.52329443
Kurtosis0.91042054
Mean0.19572668
Median Absolute Deviation (MAD)0.066663413
Skewness0.80115176
Sum98911.263
Variance0.010490406
MonotonicityNot monotonic
2025-06-09T22:33:13.974978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 621
 
0.1%
0.1873096447 163
 
< 0.1%
0.1733112066 159
 
< 0.1%
0.1918781726 157
 
< 0.1%
0.1440843421 154
 
< 0.1%
0.1453533776 146
 
< 0.1%
0.1895939086 146
 
< 0.1%
0.1305544709 143
 
< 0.1%
0.1444260055 143
 
< 0.1%
0.2389691527 142
 
< 0.1%
Other values (37815) 503380
99.6%
ValueCountFrequency (%)
0 621
0.1%
0.009415267474 1
 
< 0.1%
0.009468957439 1
 
< 0.1%
0.009820382663 1
 
< 0.1%
0.01019133151 2
 
< 0.1%
0.01024990238 1
 
< 0.1%
0.0102645451 1
 
< 0.1%
0.01035240141 1
 
< 0.1%
0.01035728231 1
 
< 0.1%
0.01039144865 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9947676689 1
< 0.1%
0.9815989848 1
< 0.1%
0.9556618508 1
< 0.1%
0.9448408825 1
< 0.1%
0.9239408434 1
< 0.1%
0.9062524405 1
< 0.1%
0.9025380711 1
< 0.1%
0.875204998 1
< 0.1%
0.8654822335 1
< 0.1%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56133517
Minimum0
Maximum1
Zeros1266
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:14.062488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31102362
Q10.47244094
median0.56299213
Q30.66141732
95-th percentile0.7992126
Maximum1
Range1
Interquartile range (IQR)0.18897638

Descriptive statistics

Standard deviation0.14874626
Coefficient of variation (CV)0.26498653
Kurtosis0.54411121
Mean0.56133517
Median Absolute Deviation (MAD)0.094488189
Skewness-0.29140078
Sum283672.97
Variance0.02212545
MonotonicityNot monotonic
2025-06-09T22:33:14.146694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.562992126 6582
 
1.3%
0.5708661417 6474
 
1.3%
0.5433070866 6384
 
1.3%
0.5748031496 6243
 
1.2%
0.5590551181 6189
 
1.2%
0.5354330709 6166
 
1.2%
0.5472440945 6153
 
1.2%
0.5866141732 6078
 
1.2%
0.531496063 6035
 
1.2%
0.5905511811 6026
 
1.2%
Other values (245) 443024
87.7%
ValueCountFrequency (%)
0 1266
0.3%
0.003937007874 14
 
< 0.1%
0.007874015748 15
 
< 0.1%
0.01181102362 15
 
< 0.1%
0.0157480315 18
 
< 0.1%
0.01968503937 18
 
< 0.1%
0.02362204724 24
 
< 0.1%
0.02755905512 28
 
< 0.1%
0.03149606299 20
 
< 0.1%
0.03543307087 31
 
< 0.1%
ValueCountFrequency (%)
1 4
 
< 0.1%
0.9960629921 8
 
< 0.1%
0.9921259843 16
 
< 0.1%
0.9881889764 10
 
< 0.1%
0.9842519685 15
 
< 0.1%
0.9803149606 35
< 0.1%
0.9763779528 41
< 0.1%
0.9724409449 54
< 0.1%
0.968503937 68
< 0.1%
0.9645669291 78
< 0.1%

Distance_to_Water
Real number (ℝ)

High correlation  Zeros 

Distinct48761
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19219802
Minimum0
Maximum1
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:14.230429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.021142888
Q10.076231767
median0.1591766
Q30.27493072
95-th percentile0.48581219
Maximum1
Range1
Interquartile range (IQR)0.19869896

Descriptive statistics

Standard deviation0.15196498
Coefficient of variation (CV)0.7906688
Kurtosis1.604842
Mean0.19219802
Median Absolute Deviation (MAD)0.095556376
Skewness1.1832935
Sum97128.036
Variance0.023093354
MonotonicityNot monotonic
2025-06-09T22:33:14.310432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21630
 
4.3%
0.02114288811 4151
 
0.8%
0.02124833958 3374
 
0.7%
0.0211546309 3338
 
0.7%
0.02118982022 3333
 
0.7%
0.02132999697 2748
 
0.5%
0.02143452793 2308
 
0.5%
0.02156159981 2177
 
0.4%
0.02171081682 1810
 
0.4%
0.02188172591 1299
 
0.3%
Other values (48751) 459186
90.9%
ValueCountFrequency (%)
0 21630
4.3%
0.02114288811 4151
 
0.8%
0.0211546309 3338
 
0.7%
0.02118982022 3333
 
0.7%
0.02124833958 3374
 
0.7%
0.02132999697 2748
 
0.5%
0.02143452793 2308
 
0.5%
0.02156159981 2177
 
0.4%
0.02171081682 1810
 
0.4%
0.02188172591 1299
 
0.3%
ValueCountFrequency (%)
1 1
< 0.1%
0.9960384945 1
< 0.1%
0.9944624595 1
< 0.1%
0.9919249796 1
< 0.1%
0.9851045647 1
< 0.1%
0.9831829448 1
< 0.1%
0.982764056 1
< 0.1%
0.9802502571 1
< 0.1%
0.979413088 1
< 0.1%
0.9755568789 1
< 0.1%

Wilderness_Area_0
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1.0
260796 
0.0
244558 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Length

2025-06-09T22:33:14.379942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:15.569032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Most occurring characters

ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Wilderness_Area_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496925 
1.0
 
8429

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Length

2025-06-09T22:33:15.618542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:15.656542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Wilderness_Area_2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
306193 
1.0
199161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Length

2025-06-09T22:33:15.703545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:15.741822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Most occurring characters

ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Wilderness_Area_3
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
468386 
1.0
 
36968

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Length

2025-06-09T22:33:15.790819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:15.829265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Soil_Type_0
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502323 
1.0
 
3031

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Length

2025-06-09T22:33:15.874266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:15.914958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Soil_Type_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
500436 
1.0
 
4918

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Length

2025-06-09T22:33:15.963780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.001784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Soil_Type_2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502823 
1.0
 
2531

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Length

2025-06-09T22:33:16.049292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.087292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Soil_Type_3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497735 
1.0
 
7619

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Length

2025-06-09T22:33:16.133802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.170802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Soil_Type_4
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503757 
1.0
 
1597

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Length

2025-06-09T22:33:16.217074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.256663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Soil_Type_5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498779 
1.0
 
6575

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Length

2025-06-09T22:33:16.302666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.342176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Soil_Type_6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505249 
1.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Length

2025-06-09T22:33:16.390174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.431686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Soil_Type_7
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505175 
1.0
 
179

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Length

2025-06-09T22:33:16.478420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.515931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Soil_Type_8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504207 
1.0
 
1147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Length

2025-06-09T22:33:16.570446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.618958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Soil_Type_9
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
478425 
1.0
 
26929

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Length

2025-06-09T22:33:16.663958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.700963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Soil_Type_10
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497100 
1.0
 
8254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Length

2025-06-09T22:33:16.747243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.784240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Soil_Type_11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475383 
1.0
 
29971

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Length

2025-06-09T22:33:16.828751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.865751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Soil_Type_12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
492001 
1.0
 
13353

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Length

2025-06-09T22:33:16.911410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:16.948917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Soil_Type_13
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504978 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Length

2025-06-09T22:33:16.997609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.046118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Soil_Type_14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505351 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Length

2025-06-09T22:33:17.094118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.134629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Soil_Type_15
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502813 
1.0
 
2541

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Length

2025-06-09T22:33:17.181629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.221143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Soil_Type_16
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502160 
1.0
 
3194

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Length

2025-06-09T22:33:17.272572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.311577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Soil_Type_17
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503525 
1.0
 
1829

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Length

2025-06-09T22:33:17.358083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.396083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Soil_Type_18
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
501981 
1.0
 
3373

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Length

2025-06-09T22:33:17.442595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.480595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Soil_Type_19
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496711 
1.0
 
8643

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Length

2025-06-09T22:33:17.525801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.562801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Soil_Type_20
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504516 
1.0
 
838

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Length

2025-06-09T22:33:17.608804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.647310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Soil_Type_21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
479672 
1.0
 
25682

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Length

2025-06-09T22:33:17.692311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.728822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Soil_Type_22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
456668 
1.0
48686 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Length

2025-06-09T22:33:17.773585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.811591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Soil_Type_23
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
488395 
1.0
 
16959

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Length

2025-06-09T22:33:17.860103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.896795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Soil_Type_24
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505353 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Length

2025-06-09T22:33:17.944306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:17.983306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Soil_Type_25
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503040 
1.0
 
2314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Length

2025-06-09T22:33:18.039569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.077569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Soil_Type_26
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504801 
1.0
 
553

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Length

2025-06-09T22:33:18.123080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.160080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Soil_Type_27
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504408 
1.0
 
946

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Length

2025-06-09T22:33:18.205083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.243327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Soil_Type_28
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
390180 
1.0
115174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Length

2025-06-09T22:33:18.287893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.326404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Soil_Type_29
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475184 
1.0
 
30170

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Length

2025-06-09T22:33:18.373405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.411408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Soil_Type_30
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
481101 
1.0
 
24253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Length

2025-06-09T22:33:18.458916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.497673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Soil_Type_31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
458914 
1.0
46440 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Length

2025-06-09T22:33:18.543185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.581183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Soil_Type_32
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
471457 
1.0
 
33897

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Length

2025-06-09T22:33:18.628695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.665695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Soil_Type_33
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504648 
1.0
 
706

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Length

2025-06-09T22:33:18.710697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.749207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Soil_Type_34
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503963 
1.0
 
1391

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Length

2025-06-09T22:33:18.794930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.831440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Soil_Type_35
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505235 
1.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Length

2025-06-09T22:33:18.876440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:18.914161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Soil_Type_36
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505056 
1.0
 
298

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Length

2025-06-09T22:33:18.962312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:19.002312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Soil_Type_37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
491835 
1.0
 
13519

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Length

2025-06-09T22:33:19.053988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:19.090989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Soil_Type_38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
494948 
1.0
 
10406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Length

2025-06-09T22:33:19.136121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:19.173121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Soil_Type_39
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498520 
1.0
 
6834

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Length

2025-06-09T22:33:19.218124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:33:19.255633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:33:19.288982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T22:33:19.335494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Interactions

2025-06-09T22:33:07.163672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.385946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:47.828762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.241214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:51.563030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:52.981354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.385997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:55.786553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.190142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:58.609803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.071064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.494132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:02.906827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.324938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:05.734326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.256185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.491282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:47.920273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.335446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:51.657310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.073473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.476506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:55.878067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.282648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:58.704710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.167171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.585637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.000829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.419449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:05.827314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.351468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.585544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.011170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.426958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:51.761823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.167709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.570019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:55.970357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.379161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:58.810910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.260504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.679147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.096130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.514963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:05.931819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.445987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.690059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.105688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.523239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:51.855137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.262224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.661156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:56.064484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.474433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:58.908424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.359010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.771801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.191237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.610250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:06.027339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.538499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.783329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.198201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.616100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:51.945647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.355640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.753671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:56.157041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.567945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:59.002203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.458135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.865311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.284538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.703249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:06.120598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.631785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.876831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.291981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.716613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:52.038757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.447145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.842182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:56.248553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.663952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:59.098388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.554423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:01.957831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.379049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.796821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:06.211703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.723305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:46.971960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.385496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.809838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:52.132048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:53.538658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:54.937513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:56.343067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.758168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:59.192903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.648933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:02.052060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.471561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.888328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:06.304434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.815528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:47.065236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:48.478004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:49.901350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:52.224558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:32:55.030669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:56.436915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:57.851680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:59.288210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:00.740567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:02.144176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:03.564825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:04.982840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:06.395948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.923805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:32:47.159753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:32:53.733167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:32:51.096722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:32:53.919963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:33:01.023862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:33:04.229678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:05.642292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:33:07.069504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T22:33:19.439004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectAvg_HillshadeCover_TypeDistance_to_WaterElevationElevation_x_SlopeHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHydro_Road_Fire_DistanceSlopeSoil_Type_0Soil_Type_1Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_2Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_3Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Vertical_Distance_To_HydrologyWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3
Aspect1.0000.4360.0400.0030.0320.0700.632-0.4150.414-0.110-0.0030.019-0.0470.0680.0530.0460.0700.0990.1460.0180.0040.0220.0190.0490.0130.0460.1030.0520.0210.0340.1540.0000.0700.0660.0730.1110.1440.1540.0950.1060.0890.0320.0270.0410.0290.0450.0390.0270.0270.0250.0070.0040.0320.1800.0720.2260.1170.1980.136
Avg_Hillshade0.4361.000-0.0780.0250.193-0.4990.655-0.1940.9860.0180.0360.2190.158-0.5230.1100.0200.0710.0870.0700.0080.0030.0180.0340.0220.0550.0340.0780.0340.0450.1430.1230.0010.0260.0150.0070.0740.0630.0450.0580.1270.1100.0210.0090.0150.0260.0490.0650.0450.0800.0140.0080.0150.0130.213-0.1090.0850.0640.0380.230
Cover_Type0.040-0.0781.0000.002-0.4930.084-0.028-0.009-0.063-0.121-0.007-0.226-0.2230.1670.2500.2930.1070.2050.1370.1640.0140.0300.1950.0630.0450.0450.4170.0520.2010.1970.0700.0000.0510.0220.0380.2060.1470.2580.0820.1020.0870.0340.1810.0420.1280.3630.3340.2220.1810.3150.0140.0100.0340.5140.1210.3450.0760.1620.817
Distance_to_Water0.0030.0250.0021.0000.2510.0790.036-0.0470.0220.0650.9990.0620.1430.0350.0330.0480.0410.0810.0160.0390.0000.0760.0940.0210.0500.1010.0280.0510.0620.1910.0410.0000.0240.1760.0370.0790.0540.0350.0690.1400.0720.1600.0440.0660.0190.0680.0440.1950.0180.0410.0200.0110.0270.0600.6470.1180.0250.1690.098
Elevation0.0320.193-0.4930.2511.000-0.0230.0730.0260.1860.1290.2610.4170.368-0.1750.3650.2250.1720.2860.1360.1300.0160.0760.1360.2310.0630.0880.2670.0260.2160.1770.0870.0010.0690.0530.0660.2340.1220.1890.0920.1870.0870.0500.1300.0470.0780.4090.3390.6280.2890.3420.0200.0270.1820.5470.0600.2880.1450.2220.926
Elevation_x_Slope0.070-0.4990.0840.079-0.0231.000-0.181-0.119-0.443-0.1320.057-0.155-0.1780.9840.0650.0440.0750.1950.2090.0100.0000.0440.0600.0670.0970.0810.0610.0250.0410.1930.1160.0070.0430.0770.0940.0630.0980.0770.0790.1160.2350.0140.0080.0140.0190.0390.1810.0950.0450.0380.0230.0360.0440.1720.3310.1890.0590.1400.147
Hillshade_3pm0.6320.655-0.0280.0360.073-0.1811.000-0.8190.566-0.0830.0350.1050.030-0.1870.1590.0210.0750.1440.2230.0060.0000.0360.0370.0520.0580.0670.1650.0500.0440.1410.0300.0070.0330.0790.1820.1000.1480.0330.0580.1320.1100.0170.0260.0130.0260.0640.0950.0450.0650.0200.0120.0170.0280.1530.0360.1990.1020.1260.208
Hillshade_9am-0.415-0.194-0.009-0.0470.026-0.119-0.8191.000-0.0860.126-0.0390.0070.067-0.1250.0520.0350.0450.1240.1390.0100.0000.0250.0240.0370.0560.0560.0880.0340.0490.1240.1230.0000.0380.0570.1590.0910.1510.0440.0880.1050.0970.0120.0260.0090.0120.0470.0580.0190.0760.0280.0120.0180.0270.307-0.1320.2310.0810.1340.278
Hillshade_Noon0.4140.986-0.0630.0220.186-0.4430.566-0.0861.0000.0200.0320.2090.152-0.4680.0940.0250.0740.0850.0690.0110.0010.0160.0300.0230.0480.0370.0480.0340.0390.1320.1350.0000.0300.0240.0080.0840.0460.0700.0550.1260.1170.0230.0080.0170.0260.0470.0590.0460.0840.0170.0070.0150.0140.235-0.0990.1030.0470.0640.236
Horizontal_Distance_To_Fire_Points-0.1100.018-0.1210.0650.129-0.132-0.0830.1260.0201.0000.0740.3710.735-0.1690.1240.0880.0760.2990.1160.0450.0040.1100.0430.1710.0270.1290.0780.0330.0810.0900.0900.0060.0800.0400.0420.2220.0740.0680.0830.1330.0910.0360.0290.0230.0380.0870.0560.0760.0700.1160.0880.0450.0560.217-0.0390.4070.1300.2830.340
Horizontal_Distance_To_Hydrology-0.0030.036-0.0070.9990.2610.0570.035-0.0390.0320.0741.0000.0720.1540.0130.0370.0470.0420.0810.0200.0390.0000.0750.0930.0190.0500.1000.0310.0510.0590.1870.0450.0000.0250.1630.0370.0790.0550.0380.0690.1430.0670.1630.0430.0700.0190.0700.0420.1870.0170.0430.0210.0110.0270.0670.6240.1130.0250.1650.107
Horizontal_Distance_To_Roadways0.0190.219-0.2260.0620.417-0.1550.1050.0070.2090.3710.0721.0000.880-0.2280.1460.1030.1250.1040.1450.0330.0050.0570.0650.0800.1050.0870.0850.0410.1500.0680.0770.0000.0920.0440.0770.3210.1160.1030.1610.1830.1340.0440.0490.0350.0500.1090.1110.0910.1100.1550.0550.0480.0680.238-0.0320.4830.1680.4220.398
Hydro_Road_Fire_Distance-0.0470.158-0.2230.1430.368-0.1780.0300.0670.1520.7350.1540.8801.000-0.2470.2100.1300.1020.2160.1520.0580.0100.0870.0660.1120.0740.1320.1280.0520.1110.0970.0810.0000.1020.0570.0520.3250.0950.1000.1180.2190.1250.0470.0310.0230.0580.1210.0960.0900.1330.1950.0650.0590.0990.3200.0020.5160.1750.4200.543
Slope0.068-0.5230.1670.035-0.1750.984-0.187-0.125-0.468-0.1690.013-0.228-0.2471.0000.1350.0190.0650.1760.2050.0040.0000.0340.0410.0460.1030.0760.1170.0260.0640.2060.1120.0040.0450.0550.0980.0900.1000.1070.0860.1410.2250.0150.0170.0170.0110.0690.0930.0420.0940.0150.0210.0360.0340.2650.3160.2160.0720.1270.311
Soil_Type_00.0530.1100.2500.0330.3650.0650.1590.0520.0940.1240.0370.1460.2100.1351.0000.0070.0100.0190.0130.0010.0000.0050.0060.0040.0060.0100.0050.0020.0180.0250.0140.0000.0050.0020.0030.0420.0190.0090.0170.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0180.0260.0800.0100.0630.276
Soil_Type_10.0460.0200.2930.0480.2250.0440.0210.0350.0250.0880.0470.1030.1300.0190.0071.0000.0130.0250.0160.0020.0000.0070.0080.0060.0080.0130.0070.0040.0230.0320.0180.0000.0060.0030.0040.0540.0250.0120.0220.0310.0270.0030.0050.0000.0010.0160.0140.0110.0050.0110.0000.0000.0040.0230.0420.1020.0130.0340.138
Soil_Type_100.0700.0710.1070.0410.1720.0750.0750.0450.0740.0760.0420.1250.1020.0650.0100.0131.0000.0320.0210.0030.0000.0090.0100.0080.0100.0170.0090.0050.0300.0420.0240.0000.0090.0040.0050.0700.0320.0160.0290.0410.0340.0040.0060.0000.0020.0210.0190.0150.0070.0150.0000.0010.0060.0310.0220.1330.0170.1410.000
Soil_Type_110.0990.0870.2050.0810.2860.1950.1440.1240.0850.2990.0810.1040.2160.1760.0190.0250.0321.0000.0410.0070.0000.0180.0200.0150.0200.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1360.0630.0310.0560.0800.0670.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0740.2430.0330.2020.071
Soil_Type_120.1460.0700.1370.0160.1360.2090.2230.1390.0690.1160.0200.1450.1520.2050.0130.0160.0210.0411.0000.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0890.0410.0200.0370.0520.0440.0060.0080.0020.0030.0270.0240.0190.0090.0190.0010.0020.0080.0390.1060.1700.0190.2040.046
Soil_Type_130.0180.0080.1640.0390.1300.0100.0060.0100.0110.0450.0390.0330.0580.0040.0010.0020.0030.0070.0041.0000.0000.0000.0010.0000.0010.0030.0000.0000.0060.0090.0050.0000.0000.0000.0000.0150.0070.0030.0060.0080.0070.0000.0000.0000.0000.0040.0030.0030.0000.0020.0000.0000.0000.0060.0150.0280.0030.0190.092
Soil_Type_140.0040.0030.0140.0000.0160.0000.0000.0000.0010.0040.0000.0050.0100.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.007
Soil_Type_150.0220.0180.0300.0760.0760.0440.0360.0250.0160.1100.0750.0570.0870.0340.0050.0070.0090.0180.0120.0000.0001.0000.0050.0040.0050.0090.0050.0020.0160.0230.0130.0000.0040.0010.0020.0390.0180.0090.0160.0230.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0420.0460.0070.0500.008
Soil_Type_160.0190.0340.1950.0940.1360.0600.0370.0240.0300.0430.0930.0650.0660.0410.0060.0080.0100.0200.0130.0010.0000.0051.0000.0040.0060.0100.0050.0030.0180.0260.0150.0000.0050.0020.0030.0430.0200.0100.0180.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0190.0440.0820.0100.0580.054
Soil_Type_170.0490.0220.0630.0210.2310.0670.0520.0370.0230.1710.0190.0800.1120.0460.0040.0060.0080.0150.0100.0000.0000.0040.0041.0000.0050.0080.0040.0010.0140.0200.0110.0000.0040.0010.0020.0330.0150.0070.0130.0190.0160.0010.0020.0000.0000.0100.0090.0070.0030.0070.0000.0000.0020.0140.0350.0580.0080.0490.017
Soil_Type_180.0130.0550.0450.0500.0630.0970.0580.0560.0480.0270.0500.1050.0740.1030.0060.0080.0100.0200.0130.0010.0000.0050.0060.0051.0000.0110.0050.0030.0190.0270.0150.0000.0050.0020.0030.0440.0210.0100.0180.0260.0220.0020.0040.0000.0000.0130.0120.0090.0040.0090.0000.0000.0030.0190.0420.0490.0130.0410.023
Soil_Type_190.0460.0340.0450.1010.0880.0810.0670.0560.0370.1290.1000.0870.1320.0760.0100.0130.0170.0330.0220.0030.0000.0090.0100.0080.0111.0000.0090.0050.0300.0430.0240.0000.0090.0040.0050.0720.0330.0160.0300.0420.0350.0050.0070.0010.0030.0220.0190.0150.0070.0150.0000.0020.0060.0310.0610.0700.0170.0470.037
Soil_Type_20.1030.0780.4170.0280.2670.0610.1650.0880.0480.0780.0310.0850.1280.1170.0050.0070.0090.0180.0120.0000.0000.0050.0050.0040.0050.0091.0000.0020.0160.0230.0130.0000.0040.0010.0020.0380.0180.0090.0160.0220.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0330.0730.0090.0530.244
Soil_Type_200.0520.0340.0520.0510.0260.0250.0500.0340.0340.0330.0510.0410.0520.0260.0020.0040.0050.0100.0060.0000.0000.0020.0030.0010.0030.0050.0021.0000.0090.0130.0070.0000.0020.0000.0000.0220.0100.0050.0090.0130.0110.0000.0010.0000.0000.0060.0060.0040.0010.0040.0000.0000.0000.0090.0240.0420.0050.0500.011
Soil_Type_210.0210.0450.2010.0620.2160.0410.0440.0490.0390.0810.0590.1500.1110.0640.0180.0230.0300.0580.0380.0060.0000.0160.0180.0140.0190.0300.0160.0091.0000.0760.0430.0000.0160.0070.0100.1260.0580.0290.0520.0740.0620.0080.0120.0030.0050.0380.0330.0270.0130.0260.0030.0040.0110.0550.0830.1150.1040.1110.065
Soil_Type_220.0340.1430.1970.1910.1770.1930.1410.1240.1320.0900.1870.0680.0970.2060.0250.0320.0420.0820.0540.0090.0000.0230.0260.0200.0270.0430.0230.0130.0761.0000.0610.0000.0220.0110.0140.1770.0820.0400.0730.1040.0880.0120.0170.0050.0080.0540.0470.0380.0180.0370.0040.0060.0150.0770.1530.0460.0950.0230.092
Soil_Type_230.1540.1230.0700.0410.0870.1160.0300.1230.1350.0900.0450.0770.0810.1120.0140.0180.0240.0470.0310.0050.0000.0130.0150.0110.0150.0240.0130.0070.0430.0611.0000.0000.0120.0060.0080.1010.0470.0230.0420.0590.0500.0070.0100.0020.0040.0310.0270.0220.0100.0210.0020.0030.0090.0440.0440.1290.0870.1370.052
Soil_Type_240.0000.0010.0000.0000.0010.0070.0070.0000.0000.0060.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0050.0000.000
Soil_Type_250.0700.0260.0510.0240.0690.0430.0330.0380.0300.0800.0250.0920.1020.0450.0050.0060.0090.0170.0110.0000.0000.0040.0050.0040.0050.0090.0040.0020.0160.0220.0120.0001.0000.0010.0020.0370.0170.0080.0150.0210.0180.0020.0030.0000.0000.0110.0100.0080.0030.0080.0000.0000.0030.0160.0190.0700.0090.0840.019
Soil_Type_260.0660.0150.0220.1760.0530.0770.0790.0570.0240.0400.1630.0440.0570.0550.0020.0030.0040.0080.0050.0000.0000.0010.0020.0010.0020.0040.0010.0000.0070.0110.0060.0000.0011.0000.0000.0180.0080.0040.0070.0100.0090.0000.0000.0000.0000.0050.0040.0030.0000.0030.0000.0000.0000.0080.1780.0340.0040.0410.009
Soil_Type_270.0730.0070.0380.0370.0660.0940.1820.1590.0080.0420.0370.0770.0520.0980.0030.0040.0050.0110.0070.0000.0000.0020.0030.0020.0030.0050.0020.0000.0100.0140.0080.0000.0020.0001.0000.0230.0110.0050.0100.0140.0110.0000.0010.0000.0000.0070.0060.0050.0010.0050.0000.0000.0010.0100.1250.0450.0050.0540.012
Soil_Type_280.1110.0740.2060.0790.2340.0630.1000.0910.0840.2220.0790.3210.3250.0900.0420.0540.0700.1360.0890.0150.0000.0390.0430.0330.0440.0720.0380.0220.1260.1770.1010.0000.0370.0180.0231.0000.1370.0670.1220.1730.1460.0200.0280.0080.0130.0900.0790.0640.0310.0620.0080.0100.0260.1290.0960.5260.0710.4380.153
Soil_Type_290.1440.0630.1470.0540.1220.0980.1480.1510.0460.0740.0550.1160.0950.1000.0190.0250.0320.0630.0410.0070.0000.0180.0200.0150.0210.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1371.0000.0310.0570.0800.0680.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0270.2440.0330.2030.071
Soil_Type_30.1540.0450.2580.0350.1890.0770.0330.0440.0700.0680.0380.1030.1000.1070.0090.0120.0160.0310.0200.0030.0000.0090.0100.0070.0100.0160.0090.0050.0290.0400.0230.0000.0080.0040.0050.0670.0311.0000.0280.0390.0330.0040.0060.0000.0020.0200.0180.0140.0070.0140.0000.0010.0060.0290.0250.1280.0160.1120.042
Soil_Type_300.0950.0580.0820.0690.0920.0790.0580.0880.0550.0830.0690.1610.1180.0860.0170.0220.0290.0560.0370.0060.0000.0160.0180.0130.0180.0300.0160.0090.0520.0730.0420.0000.0150.0070.0100.1220.0570.0281.0000.0710.0600.0080.0120.0030.0050.0370.0320.0260.0120.0260.0030.0040.0110.0530.0410.2320.0290.2780.063
Soil_Type_310.1060.1270.1020.1400.1870.1160.1320.1050.1260.1330.1430.1830.2190.1410.0250.0310.0410.0800.0520.0080.0000.0230.0250.0190.0260.0420.0220.0130.0740.1040.0590.0000.0210.0100.0140.1730.0800.0390.0711.0000.0850.0120.0170.0040.0070.0530.0460.0370.0180.0360.0040.0060.0150.0750.0490.3280.0410.3730.089
Soil_Type_320.0890.1100.0870.0720.0870.2350.1100.0970.1170.0910.0670.1340.1250.2250.0210.0270.0340.0670.0440.0070.0000.0190.0210.0160.0220.0350.0190.0110.0620.0880.0500.0000.0180.0090.0110.1460.0680.0330.0600.0851.0000.0100.0140.0040.0060.0440.0390.0310.0150.0310.0030.0050.0130.0640.1450.2770.0170.3280.075
Soil_Type_330.0320.0210.0340.1600.0500.0140.0170.0120.0230.0360.1630.0440.0470.0150.0020.0030.0040.0090.0060.0000.0000.0020.0020.0010.0020.0050.0020.0000.0080.0120.0070.0000.0020.0000.0000.0200.0090.0040.0080.0120.0101.0000.0000.0000.0000.0060.0050.0040.0010.0040.0000.0000.0000.0090.1030.0390.0040.0460.010
Soil_Type_340.0270.0090.1810.0440.1300.0080.0260.0260.0080.0290.0430.0490.0310.0170.0040.0050.0060.0130.0080.0000.0000.0030.0040.0020.0040.0070.0030.0010.0120.0170.0100.0000.0030.0000.0010.0280.0130.0060.0120.0170.0140.0001.0000.0000.0000.0080.0070.0060.0020.0060.0000.0000.0020.0120.0280.0040.0050.0140.015
Soil_Type_350.0410.0150.0420.0660.0470.0140.0130.0090.0170.0230.0700.0350.0230.0170.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0010.0000.0000.0030.0050.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0040.0000.0001.0000.0000.0020.0010.0000.0000.0000.0000.0000.0000.0030.0250.0160.0010.0190.004
Soil_Type_360.0290.0260.1280.0190.0780.0190.0260.0120.0260.0380.0190.0500.0580.0110.0000.0010.0020.0060.0030.0000.0000.0000.0000.0000.0000.0030.0000.0000.0050.0080.0040.0000.0000.0000.0000.0130.0060.0020.0050.0070.0060.0000.0000.0001.0000.0040.0030.0020.0000.0020.0000.0000.0000.0050.0060.0130.0020.0080.007
Soil_Type_370.0450.0490.3630.0680.4090.0390.0640.0470.0470.0870.0700.1090.1210.0690.0130.0160.0210.0420.0270.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0900.0420.0200.0370.0530.0440.0060.0080.0020.0041.0000.0240.0190.0090.0190.0010.0020.0080.0390.0240.0130.0140.0150.047
Soil_Type_380.0390.0650.3340.0440.3390.1810.0950.0580.0590.0560.0420.1110.0960.0930.0110.0140.0190.0360.0240.0030.0000.0100.0110.0090.0120.0190.0100.0060.0330.0470.0270.0000.0100.0040.0060.0790.0360.0180.0320.0460.0390.0050.0070.0010.0030.0241.0000.0170.0080.0170.0010.0020.0070.0340.0550.0390.0150.0140.041
Soil_Type_390.0270.0450.2220.1950.6280.0950.0450.0190.0460.0760.1870.0910.0900.0420.0090.0110.0150.0290.0190.0030.0000.0080.0090.0070.0090.0150.0080.0040.0270.0380.0220.0000.0080.0030.0050.0640.0290.0140.0260.0370.0310.0040.0060.0000.0020.0190.0171.0000.0060.0130.0000.0010.0050.0280.2410.0280.0490.0240.033
Soil_Type_40.0270.0800.1810.0180.2890.0450.0650.0760.0840.0700.0170.1100.1330.0940.0040.0050.0070.0140.0090.0000.0000.0030.0040.0030.0040.0070.0030.0010.0130.0180.0100.0000.0030.0000.0010.0310.0140.0070.0120.0180.0150.0010.0020.0000.0000.0090.0080.0061.0000.0060.0000.0000.0020.0130.0360.0580.0070.0450.200
Soil_Type_50.0250.0140.3150.0410.3420.0380.0200.0280.0170.1160.0430.1550.1950.0150.0090.0110.0150.0290.0190.0020.0000.0080.0090.0070.0090.0150.0080.0040.0260.0370.0210.0000.0080.0030.0050.0620.0290.0140.0260.0360.0310.0040.0060.0000.0020.0190.0170.0130.0061.0000.0000.0010.0050.0270.0550.1190.0150.0930.409
Soil_Type_60.0070.0080.0140.0200.0200.0230.0120.0120.0070.0880.0210.0550.0650.0210.0000.0000.0000.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0030.0000.0000.0000.0000.0010.0010.0000.0000.0001.0000.0000.0000.0030.0080.0140.0000.0110.004
Soil_Type_70.0040.0150.0100.0110.0270.0360.0170.0180.0150.0450.0110.0480.0590.0360.0000.0000.0010.0040.0020.0000.0000.0000.0000.0000.0000.0020.0000.0000.0040.0060.0030.0000.0000.0000.0000.0100.0040.0010.0040.0060.0050.0000.0000.0000.0000.0020.0020.0010.0000.0010.0001.0000.0000.0040.0110.0180.0010.0150.005
Soil_Type_80.0320.0130.0340.0270.1820.0440.0280.0270.0140.0560.0270.0680.0990.0340.0030.0040.0060.0120.0080.0000.0000.0030.0030.0020.0030.0060.0030.0000.0110.0150.0090.0000.0030.0000.0010.0260.0120.0060.0110.0150.0130.0000.0020.0000.0000.0080.0070.0050.0020.0050.0000.0001.0000.0110.0310.0460.0060.0380.013
Soil_Type_90.1800.2130.5140.0600.5470.1720.1530.3070.2350.2170.0670.2380.3200.2650.0180.0230.0310.0600.0390.0060.0000.0170.0190.0140.0190.0310.0170.0090.0550.0770.0440.0000.0160.0080.0100.1290.0600.0290.0530.0750.0640.0090.0120.0030.0050.0390.0340.0280.0130.0270.0030.0040.0111.0000.0900.2450.0310.0290.539
Vertical_Distance_To_Hydrology0.072-0.1090.1210.6470.0600.3310.036-0.132-0.099-0.0390.624-0.0320.0020.3160.0260.0420.0220.0740.1060.0150.0000.0420.0440.0350.0420.0610.0330.0240.0830.1530.0440.0050.0190.1780.1250.0960.0270.0250.0410.0490.1450.1030.0280.0250.0060.0240.0550.2410.0360.0550.0080.0110.0310.0901.0000.1940.0680.1660.106
Wilderness_Area_00.2260.0850.3450.1180.2880.1890.1990.2310.1030.4070.1130.4830.5160.2160.0800.1020.1330.2430.1700.0280.0010.0460.0820.0580.0490.0700.0730.0420.1150.0460.1290.0000.0700.0340.0450.5260.2440.1280.2320.3280.2770.0390.0040.0160.0130.0130.0390.0280.0580.1190.0140.0180.0460.2450.1941.0000.1340.8330.290
Wilderness_Area_10.1170.0640.0760.0250.1450.0590.1020.0810.0470.1300.0250.1680.1750.0720.0100.0130.0170.0330.0190.0030.0000.0070.0100.0080.0130.0170.0090.0050.1040.0950.0870.0050.0090.0040.0050.0710.0330.0160.0290.0410.0170.0040.0050.0010.0020.0140.0150.0490.0070.0150.0000.0010.0060.0310.0680.1341.0000.1050.037
Wilderness_Area_20.1980.0380.1620.1690.2220.1400.1260.1340.0640.2830.1650.4220.4200.1270.0630.0340.1410.2020.2040.0190.0000.0500.0580.0490.0410.0470.0530.0500.1110.0230.1370.0000.0840.0410.0540.4380.2030.1120.2780.3730.3280.0460.0140.0190.0080.0150.0140.0240.0450.0930.0110.0150.0380.0290.1660.8330.1051.0000.227
Wilderness_Area_30.1360.2300.8170.0980.9260.1470.2080.2780.2360.3400.1070.3980.5430.3110.2760.1380.0000.0710.0460.0920.0070.0080.0540.0170.0230.0370.2440.0110.0650.0920.0520.0000.0190.0090.0120.1530.0710.0420.0630.0890.0750.0100.0150.0040.0070.0470.0410.0330.2000.4090.0040.0050.0130.5390.1060.2900.0370.2271.000

Missing values

2025-06-09T22:33:08.683425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T22:33:09.751949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Hillshade_NoonHydro_Road_Fire_DistanceHorizontal_Distance_To_RoadwaysHillshade_9amAvg_HillshadeElevationAspectVertical_Distance_To_HydrologyHorizontal_Distance_To_HydrologyHorizontal_Distance_To_Fire_PointsSlopeElevation_x_SlopeHillshade_3pmDistance_to_WaterWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
00.9130.5320.0720.8700.9270.3690.1420.2090.1850.8750.0450.0380.5830.1821.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
10.9250.5160.0550.8660.9360.3660.1560.2010.1520.8680.0300.0250.5940.1491.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
20.9370.7260.4470.9210.9380.4730.3860.2950.1920.8530.1360.1230.5310.1941.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
30.9370.7240.4340.9370.9210.4630.4310.3640.1730.8660.2730.2450.4800.1901.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.02
40.9210.5070.0550.8660.9320.3680.1250.2080.1100.8600.0300.0250.5910.1081.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
50.9330.4830.0090.9060.9380.3600.3670.1890.2150.8410.0910.0760.5510.2121.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.02
60.8860.5410.0890.8740.8970.3740.1250.2160.1930.8720.1060.0890.5430.1901.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
70.9060.5310.0810.8740.9180.3730.1360.2180.1680.8680.0610.0510.5670.1651.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
80.8700.5400.0940.8780.8830.3790.1250.2830.1720.8700.1360.1150.5240.1741.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
90.8620.5370.0890.8980.8720.3770.1640.2240.1770.8690.1520.1270.4880.1741.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
Hillshade_NoonHydro_Road_Fire_DistanceHorizontal_Distance_To_RoadwaysHillshade_9amAvg_HillshadeElevationAspectVertical_Distance_To_HydrologyHorizontal_Distance_To_HydrologyHorizontal_Distance_To_Fire_PointsSlopeElevation_x_SlopeHillshade_3pmDistance_to_WaterWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
5053440.8430.2500.2240.8980.8500.6660.1560.2280.1360.2210.1820.1870.4610.1340.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053450.8070.2500.2240.9090.8110.6620.1670.2180.1160.2240.2420.2490.4020.1140.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053460.7990.2490.2240.8940.8040.6580.1470.2360.0960.2280.2580.2630.4090.0950.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053470.8070.2490.2240.8740.8170.6550.1220.2280.0770.2310.2270.2320.4490.0770.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053480.8230.2490.2230.8820.8320.6530.1330.2210.0610.2340.2120.2160.4570.0600.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053490.8310.2490.2230.9060.8370.6500.1690.2260.0430.2380.1970.2000.4370.0430.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053500.8430.2490.2230.9170.8480.6460.1890.2170.0210.2410.1970.2000.4370.0220.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053510.8390.2490.2240.8980.8460.6430.1560.2090.0000.2450.1970.2000.4570.0000.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01
5053520.8030.2510.2240.8700.8130.6410.1140.2090.0000.2480.2420.2450.4490.0000.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01
5053530.7870.2550.2240.8310.7990.6380.0810.2110.0210.2520.2580.2600.4720.0210.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01